基于权重稳定性，无监督选出高维数据中真正有用的特征。
Scalable unsupervised feature selection via weight stability
- 用数据自学习特征重要性，动态初始化聚类中心。
- 在多种距离指数下，关键特征权重稳定高于噪声特征。
- 算法可扩展，适合大规模高维数据的特征筛选。
无监督特征选择对提升高维数据聚类性能至关重要，因无关特征会掩盖真实结构。本文提出 Minkowski 加权 k-means++，一种新颖的初始化策略，通过数据自身推断的特征相关性概率选择聚类中心。基于此，提出两种新算法：FS-MWK++ 通过整合多个 Minkowski 指数下的特征权重，识别出稳定且信息丰富的特征；SFS-MWK++ 是基于子采样的可扩展版本。理论分析表明，在对噪声特征与聚类结构的明确假设下，相关特征在不同指数下均被赋予显著更高的权重。代码见 https://github.com/xzhang4-ops1/FSMWK。
原文摘要 · Abstract (English)
Unsupervised feature selection is critical for improving clustering performance in high-dimensional data, where irrelevant features can obscure meaningful structure. In this work, we propose the Minkowski weighted $k$-means++, a novel initialisation strategy for the Minkowski Weighted $k$-means. Our initialisation selects centroids probabilistically using feature relevance estimates derived from the data itself. Building on this, we propose two new feature selection algorithms, FS-MWK++, which aggregates feature weights across a range of Minkowski exponents identifying stable and informative features, and SFS-MWK++, a scalable variant based on subsampling. We support our approach with a theoretical analysis, demonstrating that, under explicit assumptions on noise features and cluster structure, relevant features are assigned consistently higher weights than noise features across a range of Minkowski exponents. Our software can be found at https://github.com/xzhang4-ops1/FSMWK.
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